import numpy as np
import random

import torch
from torch import Tensor
import torch.nn as nn
from typing import Type, Any, Callable, Union, List, Optional
from torch.hub import load_state_dict_from_url

device = torch.device("cuda:3")



__all__ = ['ResNet', 'resnet50']


model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-f37072fd.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-b627a593.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-0676ba61.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-63fe2227.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-394f9c45.pth',
    'resnext50_32x4d': 'https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth',
    'resnext101_32x8d': 'https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth',
    'wide_resnet50_2': 'https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth',
    'wide_resnet101_2': 'https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth',
}


def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion: int = 1

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: Tensor) -> Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
    # This variant is also known as ResNet V1.5 and improves accuracy according to
    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.

    expansion: int = 4

    def __init__(
        self,
        inplanes: int,
        planes: int,
        stride: int = 1,
        downsample: Optional[nn.Module] = None,
        groups: int = 1,
        base_width: int = 64,
        dilation: int = 1,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x: Tensor) -> Tensor:
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(
        self,
        block: Type[Union[BasicBlock, Bottleneck]],
        layers: List[int],
        num_classes: int = 5,
        zero_init_residual: bool = False,
        groups: int = 1,
        width_per_group: int = 64,
        replace_stride_with_dilation: Optional[List[bool]] = None,
        norm_layer: Optional[Callable[..., nn.Module]] = None
    ) -> None:
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(2, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)  # type: ignore[arg-type]
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)  # type: ignore[arg-type]

    def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
                    stride: int = 1, dilate: bool = False) -> nn.Sequential:
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def _forward_impl(self, x: Tensor) -> Tensor:
        # See note [TorchScript super()]
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.fc(x)

        return x

    def forward(self, x, target, use_mixup=False, mixup_alpha=0.2, layer_mix=None, mix_type=None):

        if use_mixup:

            if layer_mix == None:
                layer_mix = random.randint(0, 4)

            # 初始化
            out = x
            x_a = x
            y_a = target
            x_b = x
            y_b = target

            if layer_mix == 0:
                x_b, y_b = get_hard_neg_batch(out, target)
                out, y_mix_a, y_mix_b, lam = remix_data(out, target, x_b, y_b, mixup_alpha)

            x_a = self.conv1(x_a)
            x_a = self.bn1(x_a)
            x_a = self.relu(x_a)
            x_a = self.maxpool(x_a)
            x_a = self.layer1(x_a)

            x_b = self.conv1(x_b)
            x_b = self.bn1(x_b)
            x_b = self.relu(x_b)
            x_b = self.maxpool(x_b)
            x_b = self.layer1(x_b)

            out = self.conv1(out)
            out = self.bn1(out)
            out = self.relu(out)
            out = self.maxpool(out)

            out = self.layer1(out)

            if layer_mix == 1:
                x_b, y_b = get_hard_neg_batch(out, target)
                out, y_mix_a, y_mix_b, lam = remix_data(out, target, x_b, y_b, mixup_alpha)

            x_a = self.layer2(x_a)
            x_b = self.layer2(x_b)

            out = self.layer2(out)

            if layer_mix == 2:
                x_b, y_b = get_hard_neg_batch(out, target)
                out, y_mix_a, y_mix_b, lam = remix_data(out, target, x_b, y_b, mixup_alpha)

            x_a = self.layer3(x_a)
            x_b = self.layer3(x_b)

            out = self.layer3(out)

            if layer_mix == 3:
                x_b, y_b = get_hard_neg_batch(out, target)
                out, y_mix_a, y_mix_b, lam = remix_data(out, target, x_b, y_b, mixup_alpha)

            x_a = self.layer4(x_a)
            x_b = self.layer4(x_b)

            out = self.layer4(out)

            if layer_mix == 4:
                x_b, y_b = get_hard_neg_batch(out, target)
                out, y_mix_a, y_mix_b, lam = remix_data(out, target, x_b, y_b, mixup_alpha)

            x_a = self.avgpool(x_a)
            out_x_a = torch.flatten(x_a, 1)

            x_b = self.avgpool(x_b)
            out_x_b = torch.flatten(x_b, 1)


            out = self.avgpool(out)
            out = torch.flatten(out, 1)
            out_class = self.fc(out)



            return out_x_a, y_a, out_x_b, y_b, out, out_class, y_mix_a, y_mix_b, lam

        else:
            return self._forward_impl(x)


def _resnet(
    arch: str,
    block: Type[Union[BasicBlock, Bottleneck]],
    layers: List[int],
    pretrained: bool,
    progress: bool,
    **kwargs: Any
) -> ResNet:
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


def resnet50(pretrained: bool = False, progress: bool = True, **kwargs: Any) -> ResNet:
    r"""ResNet-50 model from
    `"Deep Residual Learning for Image Recognition" <https://arxiv.org/pdf/1512.03385.pdf>`_.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
                   **kwargs)



def distance(a, b):
    '''

    :param a: anchor
    :param b: pos or neg
    :return: distance
    '''
    re = ((a-b) * (a-b)).sum().sqrt()
    return re

def get_hard_neg_batch(batch_features, batch_labels):
    size = batch_features.size()[0]

    neg_batch_features = []
    neg_batch_labels = []

    for i in range(size):

        min_distance = float('inf')
        hard_neg_index = i

        for j in range(size):
            if i == j or batch_labels[i] == batch_labels[j]:  # 如果是同一个或者是同一类
                continue
            else:
                dis = distance(batch_features[i], batch_features[j])
                if dis.item() < min_distance:
                    min_distance = dis.item()
                    hard_neg_index = j
        neg_batch_features.append(batch_features[hard_neg_index].unsqueeze(0))
        neg_batch_labels.append(batch_labels[hard_neg_index].unsqueeze(0))

    featuresB = torch.cat(neg_batch_features)
    featuresB_labels = torch.cat(neg_batch_labels)

    return featuresB, featuresB_labels




# remix数据增强方法
def remix_data(x_a, y_a, x_b, y_b, alpha):
    '''
    :param x: batch features
    :param y: batch labels
    :param alpha: beta分布参数
    :return: mixup_inputs, pairs of targets, lambda
    '''

    num_classes = torch.tensor([487., 144., 24., 68., 20.]).float()
    k = 3.0
    tao = 0.5

    if alpha > 0:
        lam = np.random.beta(alpha, alpha)
    else:
        lam = 1.

    batch_size = x_a.size()[0]
    #
    # index = torch.randperm(batch_size).to(device)
    #
    # # 初始化
    # x_a = x
    # x_b = x[index, :]
    # y_a = y
    # y_b = y[index]

    x_rm = torch.zeros_like(x_a)
    y_rm_a = torch.zeros_like(y_a)
    y_rm_b = torch.zeros_like(y_a)

    # remix
    for i in range(0, batch_size):

        x_rm[i] = lam * x_a[i] + (1 - lam) * x_b[i]

        # y_return = lamda_y*yi + (1-lamda_y)*yj
        # lamda_y=0, y_return=yj
        if ((num_classes[y_a[i].item()] / num_classes[y_b[i].item()]) >= k) and (lam < tao):
            y_rm_a[i] = y_b[i]
            y_rm_b[i] = y_b[i]

        # lamda_y=1, y_return=yi
        elif ((num_classes[y_a[i].item()] / num_classes[y_b[i].item()]) <= (1.0 / k)) and ((1 - lam) < tao):
            y_rm_a[i] = y_a[i]
            y_rm_b[i] = y_a[i]

        else:
            y_rm_a[i] = y_a[i]
            y_rm_b[i] = y_b[i]

    return x_rm, y_rm_a, y_rm_b, lam

